Phoneme Classification Using Naïve Bayes Classifier in Reconstructed Phase Space
Format of Original
Institute of Electrical and Electronics Engineers (IEEE)
Proceedings of 2002 IEEE 10th Digital Signal Processing Workshop, and the 2nd Signal Processing Education Workshop
A novel method for classifying speech phonemes is presented. Unlike traditional cepstral based methods, this approach uses histograms of reconstructed phase spaces. A naive Bayes classifier uses the probability mass estimates for classification. The approach is verified using isolated fricative, vowel, and nasal phonemes from the TIMIT corpus. The results show that a reconstructed phase space approach is a viable method for classification of phonemes, with the potential for use in a continuous speech recognition system.